def test_profile_spat():
"""
Test that bspline_profile (1) is successful and (2) produces the
same result for a set of data fit spatially.
"""
# Files created using `rmtdict` branch (30 Jan 2020)
files = [data_path('gemini_gnirs_32_{0}_spat_fit.npz'.format(slit)) for slit in [0,1]]
for f in files:
d = np.load(f)
spat_bspl = bspline.bspline(d['spat_coo_data'], nord=4,
bkspace=np.fmax(1.0/d['median_slit_width']/10.0,
1.2*np.median(np.diff(d['spat_coo_data']))))
spat_bspl, spat_gpm_fit, spat_flat_fit, _, exit_status \
= fitting.bspline_profile(d['spat_coo_data'], d['spat_flat_data'],
np.ones_like(d['spat_flat_data']),
np.ones_like(d['spat_flat_data']), nord=4, upper=5.0, lower=5.0,
fullbkpt=spat_bspl.breakpoints, quiet=True)
assert np.allclose(d['spat_flat_fit'], spat_flat_fit), 'Bad spatial bspline result'
def test_profile_twod():
"""
Test that bspline_profile (1) is successful and (2) produces the
same result for a set of data fit two-dimensionally.
"""
# Files created using `rmtdict` branch (30 Jan 2020)
files = [data_path('gemini_gnirs_32_{0}_twod_fit.npz'.format(slit)) for slit in [0,1]]
spec_samp_coarse = 50.0
twod_sigrej = 4.0
for f in files:
d = np.load(f)
twod_bspl, twod_gpm_fit, twod_flat_fit, _ , exit_status \
= fitting.bspline_profile(d['twod_spec_coo_data'], d['twod_flat_data'],
d['twod_ivar_data'], d['poly_basis'], ingpm=d['twod_gpm_data'],
nord=4, upper=twod_sigrej, lower=twod_sigrej,
kwargs_bspline={'bkspace': spec_samp_coarse},
kwargs_reject={'groupbadpix': True, 'maxrej': 10}, quiet=True)
assert np.allclose(d['twod_flat_fit'], twod_flat_fit), 'Bad 2D bspline result'
def test_profile_spec():
"""
Test that bspline_profile (1) is successful and (2) produces the
same result for a set of data fit spectrally.
"""
# Files created using `rmtdict` branch (30 Jan 2020)
files = [data_path('gemini_gnirs_32_{0}_spec_fit.npz'.format(slit)) for slit in [0,1]]
logrej = 0.5
spec_samp_fine = 1.2
for f in files:
d = np.load(f)
spec_bspl, spec_gpm_fit, spec_flat_fit, _, exit_status \
= fitting.bspline_profile(d['spec_coo_data'], d['spec_flat_data'], d['spec_ivar_data'],
np.ones_like(d['spec_coo_data']), ingpm=d['spec_gpm_data'],
nord=4, upper=logrej, lower=logrej,
kwargs_bspline={'bkspace': spec_samp_fine},
kwargs_reject={'groupbadpix': True, 'maxrej': 5}, quiet=True)
assert np.allclose(d['spec_flat_fit'], spec_flat_fit), 'Bad spectral bspline result'
def test_io():
"""
Test that bspline_profile (1) is successful and (2) produces the
same result for a set of data fit spectrally.
"""
# Files created using `rmtdict` branch (30 Jan 2020)
files = [
data_path('gemini_gnirs_32_{0}_spec_fit.npz'.format(slit))
for slit in [0, 1]
]
logrej = 0.5
spec_samp_fine = 1.2
for f in files:
d = np.load(f)
spec_bspl, spec_gpm_fit, spec_flat_fit, _, exit_status \
= fitting.bspline_profile(d['spec_coo_data'], d['spec_flat_data'], d['spec_ivar_data'],
np.ones_like(d['spec_coo_data']), ingpm=d['spec_gpm_data'],
nord=4, upper=logrej, lower=logrej,
kwargs_bspline={'bkspace': spec_samp_fine},
kwargs_reject={'groupbadpix': True, 'maxrej': 5}, quiet=True)
# Write
ofile = data_path('tst_bspline.fits')
if os.path.isfile(ofile):
os.remove(ofile)
spec_bspl.to_file(ofile)
# Read
_spec_bspl = bspline.bspline.from_file(ofile)
# Check that the data read in is the same
assert np.array_equal(_spec_bspl.breakpoints,
spec_bspl.breakpoints), 'Bad read'
# Evaluate the bsplines and check that the written and read in data
# provide the same result
tmp = spec_bspl.value(np.linspace(0., 1., 100))[0]
_tmp = _spec_bspl.value(np.linspace(0., 1., 100))[0]
assert np.array_equal(tmp, _tmp), 'Bad bspline evaluate'
# Test overwrite
_spec_bspl.to_file(ofile, overwrite=True)
# None
bspl = bspline.bspline(None)
bspl.to_file(ofile, overwrite=True)
os.remove(ofile)